GPU Accelerated Compact-Table Propagation

TL;DR

This paper introduces a GPU-accelerated version of the Compact-Table algorithm for constraint programming, significantly improving the handling of large table constraints by leveraging modern GPU computational power.

Contribution

It presents the design and implementation of GPU-accelerated Compact-Table, integrating it into a constraint solver and demonstrating its effectiveness on large problem instances.

Findings

GPU acceleration significantly speeds up table constraint propagation.

The GPU-based approach handles larger constraints more efficiently.

Experimental results show improved performance on real-world instances.

Abstract

Constraint Programming developed within Logic Programming in the Eighties; nowadays all Prolog systems encompass modules capable of handling constraint programming on finite domains demanding their solution to a constraint solver. This work focuses on a specific form of constraint, the so-called table constraint, used to specify conditions on the values of variables as an enumeration of alternative options. Since every condition on a set of finite domain variables can be ultimately expressed as a finite set of cases, Table can, in principle, simulate any other constraint. These characteristics make Table one of the most studied constraints ever, leading to a series of increasingly efficient propagation algorithms. Despite this, it is not uncommon to encounter real-world problems with hundreds or thousands of valid cases that are simply too many to be handled effectively with standard CPU-based approaches. In this paper, we deal with the Compact-Table (CT) algorithm, the state-of-the-art propagation algorithms for Table. We describe how CT can be enhanced by exploiting the massive computational power offered by modern GPUs to handle large Table constraints. In particular, we report on the design and implementation of GPU-accelerated CT, on its integration into an existing constraint solver, and on an experimental validation performed on a significant set of instances.